Analytics Engineer-Apple Services Engineering
Apple
Data Science
Seattle, WA, USA
USD 171,600-302,200 / year + Equity
Posted on May 29, 2026
Would you like to shape how Apple understands and improves iCloud products for hundreds of millions of users worldwide, while having the unique opportunity to impact some of the most far-reaching software applications in the world?
The iCloud Data organization within Apple Services Engineering enables iCloud users to access all their content across apps (Photos, Mail, Messages, FaceTime, Calendar, and more) on every device, all the time, through consistent, scalable, timely, accurate, and fully integrated data infrastructure. We partner closely with product, engineering, and marketing teams to surface insights that drive product decisions, and we're building the analytics foundations that help iCloud continue to grow and delight users at Apple scale. If this excites you and you're energized by turning complex data into clear product insights that drive real decisions and by using AI to automate workflows and scale that impact further we'd love to hear from you! We're looking for an exceptional Analytics Engineer with strong data modeling instincts, deep SQL and Python expertise, and the curiosity to embrace AI-native tools and approaches that multiply what a great analyst can accomplish.
- Build and own scalable, reliable data pipelines and datasets that power analytics, reporting, and experimentation across iCloud products, in partnership with Engineering, Data Science, Product, and Marketing teams.
- Partner with product managers and marketing teams to define metrics and goals, building the reporting and dashboards that keep teams informed on product health and progress.
- Design and maintain dimensional data models and schemas that are reusable, well-documented, and trusted by downstream consumers across the organization.
- Explore and apply AI-native tools and LLM-powered workflows to automate repetitive analytics tasks from pipeline monitoring and anomaly alerting to natural-language interfaces over data accelerating what the team can deliver.
- Collaborate cross-functionally to identify reporting requirements, ensure critical data is accessible and accurate, and close gaps in data coverage.
- Build visualizations and self-serve analytics tools that empower product and business stakeholders to explore data independently.
- Champion data quality, lineage, and governance across owned datasets, ensuring the data our teams rely on is accurate, well-documented, and compliant with Apple's privacy standards.
- 4+ years of experience in analytics engineering, data engineering, or a closely related analytical role, ideally working with consumer products at scale.
- Deep SQL expertise and proficiency in Python or R for data analysis, transformation, and automation.
- Experience building and maintaining data pipelines and data models using distributed computing frameworks (e.g., Spark, Presto, Trino).
- Strong understanding of data modeling principles, dimensional design, and data architecture best practices that support reliable, scalable analytics.
- Experience defining metrics and partnering with product teams to establish measurement frameworks and reporting.
- Comfort working with AI-assisted development tools and an eagerness to apply them to automate workflows, accelerate pipeline development, and scale your analytical output.
- Strong communication skills â you translate complex data findings into clear, actionable insights for both technical and non-technical audiences.
- BS or equivalent work experience in a quantitative field (Statistics, Computer Science, Mathematics, or related).
- Experience with data visualization tools (e.g., Tableau, Looker, or similar) and building self-serve analytics products for business stakeholders.
- Experience using AI coding assistants and LLM-powered tools to automate analytics workflows, accelerate SQL and pipeline development, and scale analytical output beyond what's possible manually.
- Understanding of the data lifecycle and concepts such as data lineage, governance, privacy, retention, and anonymization, particularly in a privacy-sensitive consumer product environment.
- Experience working in a complex, matrixed organization on cross-functional and cross-business projects.
- Advanced degree in a quantitative field (Statistics, Computer Science, or related).